Enterprise AI Solutions

We know we want AI but not which kind. Where do we start?

Start with the workflow, not the technology. Pick something expensive, repetitive and well understood, then describe what a good outcome looks like. That description does two things: it tells us which kind of AI fits, and it becomes the yardstick the finished system is measured against. Most engagements begin with a scoping call that ends with a recommendation, often for something smaller than what was originally asked for.

What is the difference between an AI agent, a copilot and a chatbot?

A chatbot answers questions. A copilot works alongside a person and leaves them in control. An agent completes multi-step work on its own, calling tools and systems as it goes. Cost, risk and engineering effort rise in that order, so the right answer is the least autonomous option that solves your problem.

How do we know whether a workflow needs AI at all?

If the rules can be written down and they hold, conventional software is cheaper, faster and more predictable. AI earns its cost where inputs are messy, unstructured or too varied to enumerate. In practice most workflows split, with AI handling the judgment steps and ordinary automation handling the rest, and we will tell you where that line falls before any build starts.

Can we start small rather than committing to a full programme?

Yes, and we prefer it. A single narrow workflow reaching production teaches you more about your own data, your integration surface and your internal appetite than any amount of planning. It also gives you a real cost figure to extrapolate from. Companies that begin with a department-wide programme usually spend the first two months discovering things a three-week pilot would have shown them.

Does our data need to be in order first?

Less than people expect. Retrieval systems work over documents as they are, and part of the engineering is handling inconsistency rather than requiring it be cleaned up first. What does matter is access: knowing where the data lives, who owns it and what may leave your infrastructure. Where data quality genuinely blocks a use case we will say so during scoping rather than after.

How should we budget for an AI project?

In two parts. The build is driven mainly by how many systems the work touches, since integration is where most of the engineering time goes. Then there is ongoing model usage, which scales with volume and is easy to overlook. We size both during design, because a system that works well but costs more to run than the process it replaced has not succeeded, and that outcome is predictable in advance rather than a surprise.

Will it work with the systems we already run?

Yes. Most of the work in an AI project is integration rather than modelling. We connect to the CRMs, ERPs, help desks, warehouses and internal APIs you already use, and we deal with the authentication, rate limits and legacy behaviour of those systems as they actually are rather than as their documentation describes them.

How do you handle data security and compliance?

Data boundaries are decided at architecture stage: what may reach a model provider, what stays inside your infrastructure, and who can access what. Encryption, retention and audit logging follow from that. To be precise about our position, Zyneto builds compliance-ready systems and does not itself hold SOC 2 or ISO 27001 certification. The compliance obligation remains yours, and our job is engineering a system that satisfies it.

Do you work with clients outside India?

Yes. We are headquartered in Jaipur and deliver worldwide, with clients across the United States, Europe and the Gulf. Our working day overlaps almost entirely with Gulf business hours and covers the European morning. For North American clients we agree a defined daily sync window at the start of the engagement rather than leaving timezone overlap vague.

What happens on a scoping call?

You describe the workflow and we ask what it costs you today, where it breaks, and what a good outcome looks like. You leave with a recommendation on which approach fits, a rough sense of build effort and running cost, and an honest answer on whether an off-the-shelf tool would serve you better. There is no obligation attached and we would rather tell you not to build something than sell you the wrong thing.